The Properties of Convective Generating Cells Embedded in the Stratiform Cloud on Basis of Airborne Ka-Band Precipitation Cloud Radar and Droplet Measurement Technologies
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摘要: 利用机载Ka波段云雷达（Airborne Ka-Band Precipitation Cloud Radar, KPR）和粒子测量系统（Droplet Measurement Technologies, DMT），分析了2018年4月22日黄淮气旋背景系统下积层混合云中对流泡的动力和微物理特征。首先，对Ka波段云雷达观测的山东地区春季36个对流泡样本按照回波强度、水平尺度、回波顶高三个参量进行统计，结果表明平均回波强度为20～30 dBZ的对流泡占69%。对流泡水平尺度为15～30 km，占61%。对流泡最大回波顶高集中在6～8 km，比周边层云高2～4 km。之后，对4月22日积层混合云中的对流泡个例微物理参数进行统计，结果表明对流泡内部以上升气流为主，最大上升气流速度达到1.35 m s−1，平均上升气流速度为0.22 m s−1；对流泡内过冷水含量比较高，最大含水量为0.34 g m−3，平均含水量为0.15 g m−3。对流泡内冰晶数浓度是泡外的5.5倍，平均直径是泡外的1.7倍。结合云粒子图像探头，发现对流泡前沿和尾部冰粒子以柱状和辐枝状为主，而对流泡核心区域冰粒子以聚合体形式存在。冰粒子通过凇附过程和碰并过程增长，过冷水含量不足时冰粒子的凇附增长形成柱状粒子，含量充足时可迅速凇附成霰粒子。对流泡内降水形成的微物理机制不完全相同，主要依赖过冷水含量。当云中有充足的过冷水分布时，高层冰晶通过凇附增长形成霰粒子，通过融化层后形成降水；当云中缺少过冷水时，降水的形成主要通过水汽凝华过程形成冰雪晶，然后雪晶通过聚合过程实现增长。
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关键词:
- 对流泡 /
- Ka波段云雷达（KPR） /
- 粒子测量系统（DMT） /
- 降水机制
Abstract: On the basis of airborne Ka-band precipitation cloud radar (KPR) and droplet measurement technologies (DMT), the dynamic and microphysical characteristics of convective generating cells (GCs) embedded in stratiform clouds initiated by the Huanghuai cyclone on April 22, 2018 were analyzed. First, a total of 36 GCs were observed by KPR in spring in Shandong Province. The results based on the echo intensity, horizontal scale, and echo top height of these GCs show that the average echo intensity of GCs is concentrated at 20 to 30 dBZ, accounting for 69%. The horizontal scale of GCs is concentrated at 15 to 30 km, accounting for 61%. The echo top height of GCs is concentrated at 6 to 8 km, which is 2 to 4 km higher than the surrounding stratiform clouds. Afterward, the microphysical parameters of GCs in mixed-phase cumulus clouds on April 22, 2018 were counted. The results showed that the inner part of GCs is dominated by updraft with the maximum wind speed of 1.35 m s−1 and average updraft of 0.22 m s−1. GCs have high supercooled water content with the maximum of 0.34 g m−3 and average of 0.15 g m−3. The ice particle concentration in the inner part of GCs is 5.5 times that of its outer part, and the mean diameter of the inner part of GCs is 1.7 times that of its outer part. The images sampled by the cloud image probe showed that the ice particles on the head and tail of GCs were mainly columnar and radial, respectively, whereas the ice particles in the core of GCs were polymers. The growth of ice crystals depended on the accretion and collision processes. The ice crystals formed columns when the supercooled water was insufficient; otherwise, they rapidly formed graupels. The microphysical formation mechanism of precipitation in GCs is different and strongly depends on the supercooled water content. When the supercooled water content of the cloud was sufficient, graupels were rapidly formed, and surface precipitation was formed after they passed through the melting layer. When the supercooled water content of the cloud was insufficient, the formation of precipitation depended on the water vapor deposition and aggregation processes. -
图 1 机载探测设备示意图：（a）Ka波段云雷达（KPR）；（b）粒子测量系统（DMT）
Figure 1. Schematic of airborne detection equipment: (a) Airborne Ka-band precipitation cloud radar (KPR); (b) Droplet measurement technologies (DMT)
图 2 2018年4月22日（a）云雷达反射率（单位：dBZ）及（b）多普勒速度谱宽（单位：m s−1）。BJT：北京时
Figure 2. (a) Cloud radar reflectivity (units: dBZ) and (b) Doppler velocity spectral width (units: m s−1) on 22 April 2018
图 3 2018年4月22日飞行轨迹、环境温度和露点温度示意图
Figure 3. Diagram of flight path, air temperature, and dew point temperature on 22 April 2018
图 4 2018年4月22日10:37～10:51各物理量随时间的分布：（a）雷达反射率因子；（b）液态水含量（黑色实线）及垂直风速（蓝色实线）；（c）云粒子探头（CDP）观测结果（黑色实线：粒子数浓度；红色实线：粒子平均直径）；（d）云粒子图像探头（CIP）观测结果（黑色：粒子数浓度；红色：粒子平均直径）
Figure 4. Time distribution of physical quantities from 1037 BJT to 1051 BJT 22 April 2018: (a) Radar reflectivity; (b) liquid water content (black solid line) and vertical wind speed (blue solid line); (c) cloud droplet probe (CDP) observations (black solid line: particle number concentration; red solid line: mean particle diameter); (d) cloud imaging probe (CIP) observations (black solid line: particle number concentration; red solid line: mean particle diameter)
图 5 2018年4月22日10:37～10:51粒子组合谱分布
Figure 5. Distribution of particle concentration spectrum from 1037 BJT to 1051 BJT 22 April 2018. GCs: Convective generating cells
图 6 2018年4月22日15:15～15:22各物理量随时间分布：（a）雷达反射率因子；（b）液态水含量及垂直风速（黑色：液态水含量，蓝色：垂直风速）；（c）CDP观测结果（黑色：粒子数浓度；红色：粒子平均直径）；（d）CIP观测结果（黑色：粒子数浓度；红色：粒子平均直径）
Figure 6. Time distribution of physical quantities from 1515 BJT to 1522 BJT 22 April 2018: (a) Radar reflectivity; (b) liquid water content and vertical wind speed (black: liquid water content; blue: vertical wind speed); (c) CDP observations (black: particle number concentration; red: mean particle diameter); (d) CIP observations (black: particle number concentration; red: mean particle diameter)
图 7 2018年4月22日CIP谱分布对比图
Figure 7. CIP spectrum distribution comparison diagram on 22 April 2018. GC (A): GC at 1518 BJT; GC (B): GC at 1521 BJT
图 8 2018年4月22日10:44～10:49飞行轨迹上CIP探头拍摄的典型粒子图像
Figure 8. Typical particle images taken by the CIP probe on the flight path from 1044 BJT to 1049 BJT 22 April 2018
图 9 2018年4月22日10:44～10:49飞行轨迹上空气温度和露点温度变化曲线
Figure 9. Air temperatureand dew point temperature change curves on the flight trajectory from 1044 BJT to 1049 BJT 22 April 2018
图 10 2018年4月22日15:15～15:22飞行轨迹上CIP和PIP探头拍摄的典型粒子图像
Figure 10. Typical particle images taken by the CIP and PIP probes on the flight path from 1515 BJT to 1522 BJT 22 April 2018
图 11 2018年4月22日机载云雷达垂直廓线：（a）雷达反射率因子Z；（b）多普勒速度v；（c）多普勒速度谱宽σ
Figure 11. Vertical profiles for airborne cloud radar on 22 April 2018: (a) Radar reflectivity factor Z; (b) Doppler velocity v; (c) Doppler velocity spectral width σ
表 1 Ka波段云雷达（KPR）核心参数
Table 1. Ka-band precipitation cloud radar (KPR) key parameters
参数名称 参数值范围 工作频率 35.64 GHz±30 MHz 发射功率 峰值功率10 W，约5%占空比 发射功率损耗 约1 dB 脉冲宽度 0.1～20 μs 发射波形 交替长脉冲/短脉冲 传输偏振 线性偏振 脉冲重复频率 20 kHz 天线原理 上下指向的线性极化平板阵列 天线带宽 35.5～35.9 GHz 天线罩材料 聚苯乙烯（单向损耗0.1 dB） 天线外形 直径14 cm，4.2°半功率波束宽度 天线增益 32.5 dB 第一旁瓣电平 −23 dB 接收器类型 单宽带射频 接收机噪声系数 约4 dB 雷达中频频率 90/150 MHz 数字接收机 双通道，16位ADC 动态范围 90 dB@1 MHz带宽 
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表 2 粒子测量系统（DMT）设备功能及参数
Table 2. Droplet measurement technologies (DMT) equipment function and parameters
仪器名称 设备功能 测量量程 分辨率及精度 云凝结核计数器CCN-200 测量不同过饱和度下云凝结核的浓度，并可在同一时刻测量两个不同的过饱和度下的云凝结核的浓度。 量程：0.1%～2%
尺度：0.75～10 μm
通道数量：20— 被动腔气溶胶探头PCASP 测量固定档范围的大气气溶胶粒子个数、尺度及气溶胶粒子谱等。 尺度：0.1～3.0 μm
通道数量：30分辨率：0.01 μm，0.02 μm，0.1 μm，0.2 μm 云粒子组合探头CCP 测量云和降水粒子的谱分布及数浓度，并给出降水粒子的二维图像。 尺度：2～50 μm, 25～1550 μm
LWC：0.01～3 g m−3测量粒子分辨率：2 μm，25 μm
LWC分辨率：0.01 g m−3降水粒子探头PIP 测量降水粒子的谱分布及数浓度，并给出降水粒子的二维图像。 尺度：100～6400 μm
通道数：62分辨率：100 μm 综合气象要素测量系统AIMMS 测量飞行高度、经纬度、温度、气压、湿度、风速、风向、垂直风速、飞行、动压和飞机姿态等参数。 高度：0～15 km；温度：−20～+40°C，−40～+40°C（特殊要求）；静压：0～110 kPa；动压：0～14 kPa；侧分压：−7～7 kPa；相对湿度：0～100%；加速度：−5～5 g；倾斜度：−60°～60°s−1 测温精度：0.05°C；风速精度：0.5 m s−1
行测温精度：0.3°C；相对湿度精度：2%热线含水量仪LWC 测量液态水含量 0～3 g m−3 — 积冰探测仪器 测量云中积冰厚度、速度等。 标准冰厚跳变点：0.5 mm±25%
温度：−54°C～54°C— 等速进样系统 实现在增压舱飞机中对机外晴天干空气环境观测，进气采样采用尖罩进气口，可从50～150 m s−1气流中等速采样，适合1英寸外径管，仪器硬件为阳极氧化纯铝。 流速：50～150 m s−1 —
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表 3 对流泡特征统计表
Table 3. Statistical table of the characteristics of convective generating cells
日期 对流泡发生频率 Z=10～20 dBZ，Z=20～30 dBZ
Z=30～40 dBZL=1～
15 kmL=15～
30 kmL=30～
45 kmHtop=6～
8 kmHtop=8～
10 kmHtop=10～
12 km4月22日09:30～11:30 14.3% 71.4% 14.3% 57.1% 28.6% 14.3% 85.7% 14.3% 0% 4月22日14:30～16:30 37.5% 62.5% 0% 25% 50% 25% 37.5% 62.5% 0% 5月5日12:00～13:30 12.5% 81.25% 6.25% 12.5% 87.5% 0% 100% 0% 0% 5月21日12:30～13:50 20% 40% 40% 20% 40% 40% 0% 80% 20% 总计 19.4% 69.5% 11.1% 25% 61.1% 13.9% 69.4% 27.8% 2.8% 注：Z表示对流泡回波强度，L表示对流泡水平尺度，Htop表示对流泡回波顶高。
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表 4 不同形状冰晶凇附过程所需最小直径
Table 4. Minimum diameter required for riming of ice crystals of different shapes
柱状冰晶 片状冰晶 辐射状冰晶 最小直径 70 μm 220 μm 400 μm
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[1] Boucher R J. 1959. Synoptic-physical implications of 1.25-cm vertical-beam radar echoes [J]. J. Meteor., 16(3): 312−326. doi:10.1175/1520-0469(1959)016<0312:SPIOCV>2.0.CO;2 [2] Carbone R E, Bohne A R. 1975. Cellular snow generation—A Doppler radar study [J]. J. Atmos. Sci., 32(7): 1384−1394. doi:10.1175/1520-0469(1975)032<1384:CSGDRS>2.0.CO;2 [3] Douglas R H, Gunn K L S, Marshall J S. 1957. Pattern in the vertical of snow generation [J]. J. Meteor., 14(2): 95−114. doi:10.1175/1520-0469(1957)014<0095:pitvos>2.0.CO;2 [4] 段艺萍, 刘寿东, 刘黎平, 等. 2013. 利用云雷达反演层状云空气垂直速度及微物理参数的个例研究 [J]. 科学技术与工程, 13(27): 7933−7940. doi: 10.3969/j.issn.1671-1815.2013.27.003Duan Yiping, Liu Shoudong, Liu Liping, et al. 2013. A case test of retrieving stratus vertical velocity & microphysical parameters from millimeter-wave cloud radar [J]. Science Technology and Engineering (in Chinese), 13(27): 7933−7940. doi: 10.3969/j.issn.1671-1815.2013.27.003 [5] Evans A G, Locatelli J D, Stoelinga M T, et al. 2005. The IMPROVE-1 storm of 1–2 February 2001. Part Ⅱ: Cloud structures and the growth of precipitation [J]. J. Atmos. Sci., 62(10): 3456−3473. doi: 10.1175/JAS3547.1 [6] 范烨, 郭学良, 张佃国, 等. 2010. 北京及周边地区2004年8、9月层积云结构及谱分析飞机探测研究 [J]. 大气科学, 34(6): 1187−1200. doi: 10.3878/j.issn.1006-9895.2010.06.12Fan Ye, Guo Xueliang, Zhang Dianguo, et al. 2010. Airborne particle measuring system measurement on structure and size distribution of stratocumulus during August to September in 2004 over Beijing and its surrounding areas [J]. Chinese Journal of Atmospheric Sciences (in Chinese), 34(6): 1187−1200. doi: 10.3878/j.issn.1006-9895.2010.06.12 [7] Herzegh P H, Hobbs P V. 1980. The mesoscale and microscale structure and organization of clouds and precipitation in midlatitude cyclones. II: Warm-frontal clouds [J]. J. Atmos. Sci., 37(3): 597−611. doi:10.1175/1520-0469(1980)037<0597:TMAMSA>2.0.CO;2 [8] Hobbs P V, Locatelli J D. 1978. Rainbands, precipitation cores and generating cells in a cyclonic storm [J]. J. Atmos. Sci., 35(2): 230−241. doi:10.1175/1520-0469(1978)035<0230:RPCAGC>2.0.CO;2 [9] Hobbs P V, Matejka T J, Herzegh P H, et al. 1980. The mesoscale and microscale structure and organization of clouds and precipitation in midlatitude cyclones. I: A case study of a cold front [J]. J. Atmos. Sci., 37(3): 568−596. doi:10.1175/1520-0469(1980)037<0568:TMAMSA>2.0.CO;2 [10] 黄毅梅, 周毓荃, 杨敏. 2017. 利用3 mm云雷达资料分析混合相云垂直结构及过冷水分布 [J]. 高原气象, 36(1): 219−228. doi: 10.7522/j.issn.1000-0534.2015.00119Huang Yimei, Zhou Yuquan, Yang Min. 2017. Using 3 mm cloud radar data to analyze frontal mixed cloud vertical structure and supercooled water [J]. Plateau Meteorology (in Chinese), 36(1): 219−228. doi: 10.7522/j.issn.1000-0534.2015.00119 [11] Ikeda K, Rasmussen R M, Hall W D, et al. 2007. Observations of freezing drizzle in extratropical cyclonic storms during IMPROVE-2 [J]. Atmos. Sci., 64(9): 3016−3043. doi: 10.1175/JAS3999.1 [12] Illingworth A J, Hogan R J, O’Connor E J, et al. 2007. Cloudnet: Continuous evaluation of cloud profiles in seven operational models using ground-based observations [J]. Bull. Amer. Meteor. Soc., 88(6): 883−898. doi: 10.1175/BAMS-88-6-883 [13] Jackson R C, McFarquhar G M. 2014. An assessment of the impact of antishattering tips and artifact removal techniques on bulk cloud ice microphysical and optical properties measured by the 2D cloud probe [J]. J. Atmos. Oceanic Technol., 31(10): 2131−2144. doi: 10.1175/JTECH-D-14-00018.1 [14] Kollias P, Albrecht B. 2005. Why the melting layer radar reflectivity is not bright at 94 GHz [J]. Geophys. Res. Lett., 32(24): L24818. doi: 10.1029/2005gl024074 [15] Korolev A V, Emery E F, Strapp J W, et al. 2013. Quantification of the effects of shattering on airborne ice particle measurements [J]. J. Atmos. Oceanic Technol., 30(11): 2527−2553. doi: 10.1175/JTECH-D-13-00115.1 [16] Langleben M P. 1956. The plan pattern of snow echoes at the generating level [J]. J. Meteor., 13(6): 554−560. doi:10.1175/1520-0469(1956)013<0554:tppose>2.0.CO;2 [17] Lehtinen K, Higdon J L. 2003. Centimeter wavelength continuum observations of young stellar objects in the dark cloud DC 303.8–14.2 [J]. Astronomy & Astrophysics, 398(2): 583−587. doi: 10.1051/0004-6361:20021563 [18] 刘黎平, 宗蓉, 齐彦斌, 等. 2012. 云雷达反演层状云微物理参数及其与飞机观测数据的对比 [J]. 中国工程科学, 14(9): 64−71. doi: 10.3969/j.issn.1009-1742.2012.09.008Liu Liping, Zong Rong, Qi Yanbin, et al. 2012. Microphysical parameters retrieval by cloud radar and comparing with aircraft observation in stratiform cloud [J]. Engineering Science (in Chinese), 14(9): 64−71. doi: 10.3969/j.issn.1009-1742.2012.09.008 [19] Marshall J S. 1953. Precipitation trajectories and patterns [J]. J. Meteor., 10(4): 262−269. doi:10.1175/1520-0469(1953)010<0025:PTAP>2.0.CO;2 [20] Matejka T J, Houze Jr R A, Hobbs P V. 1980. Microphysics and dynamics of clouds associated with mesoscale rainbands in extratropical cyclones [J]. Quart. J. Roy. Meteor. Soc., 106(447): 29−56. doi: 10.1002/qj.49710644704 [21] 彭亮, 陈洪滨, 李柏. 2012. 3 mm多普勒云雷达测量反演云内空气垂直速度的研究 [J]. 大气科学, 36(1): 1−10. doi: 10.3878/j.issn.1006-9895.2012.01.01Peng Liang, Chen Hongbin, Li Bai. 2012. A case study of deriving vertical air velocity from 3-mm cloud radar [J]. Chinese Journal of Atmospheric Sciences (in Chinese), 36(1): 1−10. doi: 10.3878/j.issn.1006-9895.2012.01.01 [22] Plummer D M, McFarquhar G M, Rauber R M, et al. 2014. Structure and statistical analysis of the microphysical properties of generating cells in the comma head region of continental winter cyclones [J]. J. Atmos. Sci., 71(11): 4181−4203. doi: 10.1175/JAS-D-14-0100.1 [23] Rosenow A A, Plummer D M, Rauber R M, et al. 2014. Vertical velocity and physical structure of generating cells and convection in the comma head region of continental winter cyclones [J]. J. Atmos. Sci., 71(5): 1538−1558. doi: 10.1175/JAS-D-13-0249.1 [24] Rutledge S A, Hobbs P V. 1983. The mesoscale and microscale structure and organization of clouds and precipitation in midlatitude cyclones. VIII: A model for the “seeder-feeder” process in warm-frontal rainbands [J]. J. Atmos. Sci., 40(5): 71. doi:10.1175/1520-0469(1983)040<1185:TMAMSA>2.0.CO;2 [25] Sasyo Y. 1971. Study of the formation of precipitation by the aggregation of snow particles and the accretion of cloud droplets on snowflakes [J]. Pap. Meteor. Geophys., 22(2): 69−142. doi: 10.2467/mripapers1950.22.2_69 [26] Stark D, Colle B A, Yuter S E. 2013. Observed microphysical evolution for two east coast winter storms and the associated snow bands [J]. Mon. Wea. Rev., 141(6): 2037−2057. doi: 10.1175/MWR-D-12-00276.1 [27] Stokes G M, Schwartz S E. 1994. The atmospheric radiation measurement (ARM) program: Programmatic background and design of the cloud and radiation test bed [J]. Bull. Amer. Meteor. Soc., 75(7): 1201−1221. doi:10.1175/1520-0477(1994)075<1201:TARMPP>2.0.CO;2 [28] Wang P K. 2002. Ice Microdynamics [M]. New York: Academic Press, 273pp. [29] 汪学林, 秦元明, 吴宪君, 等. 2001. 层状云中对流泡特征及其在降水场中的作用 [J]. 应用气象学报, 12(S1): 146−150. doi: 10.3969/j.issn.1001-7313.2001.z1.019Wang Xuelin, Qin Yuanming, Wu Xianjun, et al. 2001. The characteristics of convective bubbles and its role in precipitation field [J]. Quarterly Journal of Applied Meteorology (in Chinese), 12(S1): 146−150. doi: 10.3969/j.issn.1001-7313.2001.z1.019 [30] Wexler R. 1955. Radar analysis of precipitation streamers observed 25 February 1954 [J]. J. Meteor., 12(4): 391−393. doi:10.1175/1520-0469(1955)012<0391:raopso>2.0.CO;2 [31] 吴举秀, 魏鸣, 周杰. 2014. 94 GHz云雷达回波及测云能力分析 [J]. 气象学报, 72(2): 402−416. doi: 10.11676/qxxb2014.001Wu Juxiu, Wei Ming, Zhou Jie. 2014. Echo and capability analysis of 94 GHz cloud radars [J]. Acta Meteor. Sinica (in Chinese), 72(2): 402−416. doi: 10.11676/qxxb2014.001 [32] 吴举秀, 魏鸣, 苏涛, 等. 2017. W波段和Ka波段云雷达探测回波对比分析 [J]. 山东气象, 37(2): 57−64. doi: 10.19513/j.cnki.issn2096-3599.2017.02.007Wu Juxiu, Wei Ming, Su Tao, et al. 2017. Comparison of the echoes detected by W-band and Ka-band cloud radars [J]. Journal of Shandong Meteorology (in Chinese), 37(2): 57−64. doi: 10.19513/j.cnki.issn2096-3599.2017.02.007 [33] 张佃国, 郭学良, 付丹红, 等. 2007. 2003年8~9月北京及周边地区云系微物理飞机探测研究 [J]. 大气科学, 31(4): 596−610. doi: 10.3878/j.issn.1006-9895.2007.04.05Zhang Dianguo, Guo Xueliang, Fu Danhong, et al. 2007. Aircraft observation on cloud microphysics in Beijing and its surrounding regions during August-September 2003 [J]. Chinese Journal of Atmospheric Sciences (in Chinese), 31(4): 596−610. doi: 10.3878/j.issn.1006-9895.2007.04.05 [34] 朱士超, 郭学良. 2014. 华北积层混合云中冰晶形状、分布与增长过程的飞机探测研究 [J]. 气象学报, 72(2): 366−389. doi: 10.11676/qxxb2014.013Zhu Shichao, Guo Xueliang. 2014. Ice crystal habits, distribution and growth process in stratiform clouds with embedded convection in North China: Aircraft measurements [J]. Acta Meteor. Sinica (in Chinese), 72(2): 366−389. doi: 10.11676/qxxb2014.013 -
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